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Machine Learning with R Cookbook, Second Edition

You're reading from   Machine Learning with R Cookbook, Second Edition Analyze data and build predictive models

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781787284395
Length 572 pages
Edition 2nd Edition
Languages
Tools
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Author (1):
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Yu-Wei, Chiu (David Chiu) Yu-Wei, Chiu (David Chiu)
Author Profile Icon Yu-Wei, Chiu (David Chiu)
Yu-Wei, Chiu (David Chiu)
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Toc

Table of Contents (21) Chapters Close

Title Page
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Practical Machine Learning with R FREE CHAPTER 2. Data Exploration with Air Quality Datasets 3. Analyzing Time Series Data 4. R and Statistics 5. Understanding Regression Analysis 6. Survival Analysis 7. Classification 1 - Tree, Lazy, and Probabilistic 8. Classification 2 - Neural Network and SVM 9. Model Evaluation 10. Ensemble Learning 11. Clustering 12. Association Analysis and Sequence Mining 13. Dimension Reduction 14. Big Data Analysis (R and Hadoop)

Using the COX proportional hazard model


The survival function gives the probability that a subject will survive up to time t where t moves to infinity. The hazard function is a rate at which an event occurs or the potential that an event will occur, per time unit. The survfit function works on the basis of the Kaplan-Meier method which works best when we have to represent two categories such as gender (male or female), as shown previously. It fails when the categories are increased.

In a Cox proportional hazards regression model, the measure of effect is the hazard rate, which is the risk of failure (the risk or probability of suffering the event of interest), given that the participant has survived up to a specific time. A probability must lie in the range 0 to 1. However, the hazard represents the expected number of events per one unit of time. As a result, the hazard in a group can exceed 1. For example, if the hazard is 0.2 at time t and the time units are months, then on average, 0.2...

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